A Database-driven Decision Support System: Customized Mortality Prediction

Journal of Personalized Medicine

Leo Anthony Celi¹  Sean Galvin²  Guido Davidzon³  Joon Lee¹  Daniel Scott¹ 

[1] Laboratory of Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Avenue, E25-505, Cambridge, MA 02139, USA; E-Mails:;Department of Cardiac Surgery, Dunedin Hospital, 201 Great King Street, Dunedin 9054, New Zealand; E-Mail:;Department of Radiology, Stanford Hospital, 300 Pasteur Drive, Stanford, CA 94305, USA; E-Mail:

关键词: decision support;    intensive care;    clinical database;    MIMIC;    informatics;   

DOI  :  10.3390/jpm2040138

来源: mdpi

【 摘 要 】

We hypothesize that local customized modeling will provide more accurate mortality prediction than the current standard approach using existing scoring systems. Mortality prediction models were developed for two subsets of patients in Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC), a public de-identified ICU database, and for the subset of patients >80 years old in a cardiac surgical patient registry. Logistic regression (LR), Bayesian network (BN) and artificial neural network (ANN) were employed. The best-fitted models were tested on the remaining unseen data and compared to either the Simplified Acute Physiology Score (SAPS) for the ICU patients, or the EuroSCORE for the cardiac surgery patients. Local customized mortality prediction models performed better as compared to the corresponding current standard severity scoring system for all three subsets of patients: patients with acute kidney injury (AUC = 0.875 for ANN, vs. SAPS, AUC = 0.642), patients with subarachnoid hemorrhage (AUC = 0.958 for BN, vs. SAPS, AUC = 0.84), and elderly patients undergoing open heart surgery (AUC = 0.94 for ANN, vs. EuroSCORE, AUC = 0.648). Rather than developing models with good external validity by including a heterogeneous patient population, an alternative approach would be to build models for specific patient subsets using one’s local database.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.